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Genda ChenView profile

Professor

• Structural Health Monitoring
• Structural Control
• Interface Mechanics and Deterioration
• +3 more

Genda Chen, Ph.D., P.E., F.ASCE, F.SEI, F.ISHMII , is a Professor and the Robert W. Abbett Distinguished Chair in Civil Engineering at Missouri University of Science and Technology (Missouri S&T) . He also serves as Director of the Center for Intelligent Infrastructure (CII) , Director of the INSPIRE University Transportation Center , and Associate Director of the Mid-America Transportation Center (MATC) . Education: Ph.D. in Civil Engineering, State University of New York at Buffalo (1992) Postdoctoral training, State University of New York at Buffalo (1993) Research Interests: His research spans structural health monitoring (SHM) , interface mechanics and deterioration , adaptive passive dampers , multi-hazards assessment , forensic studies , seismic retrofit , soil-structure interaction , and bridge engineering . His work integrates interdisciplinary collaborations with electrical, mechanical, materials, and computer engineering to advance smart infrastructure systems. Scientific Awards: 2019 SHM Person of the Year 1998 NSF CAREER Award 2004 Academy of Civil Engineers Faculty Achievement Award Missouri S&T Faculty Research Awards (2009, 2011, 2013) Inducted into the Academy of Civil Engineers at Missouri S&T (2016) Honorary member of Chi Epsilon Research Leadership: Dr. Chen has authored or co-authored over 400 technical publications , including 168 journal papers, 5 book chapters, and 24 keynote/invited presentations. He holds three patents in structural sensing and corrosion protection technologies. He led the 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-9) in 2019 in St. Louis, Missouri. Labs & Centers: Director, Center for Intelligent Infrastructure (CII) Director, INSPIRE University Transportation Center Associate Director, Mid-America Transportation Center (MATC) Director, System and Process Assessment Research Laboratory (SPAR Lab)

Mohammad FotouhiView profile

Assistant Professor

• Structural Health Monitoring
• Composite Materials
• Bio-inspired Structures
• +3 more

Mohammad Fotouhi is an Assistant Professor at Delft University of Technology's Faculty of Civil Engineering and Geosciences, specifically within the Materials, Mechanics, Management & Design (3MD) department. He has held academic positions at the University of Glasgow, University of the West of England, and University of Bristol, focusing on structural health monitoring and composite material development. PhD in Mechanical Engineering from Amirkabir University of Technology MSc and BSc in Mechanical Engineering from Amirkabir and Tabriz Universities His research spans bio-inspired composites , 4D printing , and smart materials , with emphasis on self-sensing , multi-functional composites , and damage detection . Recent work explores piezoelectric sensors for structural strain analysis and chaos-integrated neural networks for nonlinear regression. While no formal awards are listed, his publications demonstrate leadership in fatigue monitoring , impact damage detection , and hybrid material design . Collaborations with institutions like University of Bristol and ResearchGate profile highlight his academic network.

• Power Engineering
• Computational Electromagnetics
• Finite Element Method
• +6 more

Shinji Wakao is a full Professor in the School of Advanced Science and Engineering, Waseda University, Japan, where he has served since 1993 and was Dean of the School from 2016 to 2020. Holding doctoral and master degrees in electrical engineering from Waseda, he leads a prolific research group specialising in computational electromagnetics and renewable-energy power systems. Education: Doctor of Engineering, Waseda University Master of Engineering, Waseda University Bachelor of Engineering (Electrical Engineering), Faculty of Science and Engineering, Waseda University Research Interests: His work integrates large-scale numerical electromagnetic field analysis—finite-element and infinite edge-element methods, level-set topology optimisation—with practical power-engineering challenges such as photovoltaic generation forecasting, smart-grid battery scheduling, zero-energy-house design, and electric-machine optimisation. Publication Trends: Recent articles emphasise deep-learning-enhanced PV forecasting (auto-encoders, CNNs, just-in-time modelling) and advanced topology-optimisation techniques (encoder-decoder networks, MMA-accelerated level-set methods) for magnetic shields and motors, reflecting a convergence of machine learning and energy-system science. Scientific Awards: IEEJ Academic Promotion Award (2008) IEEJ Distinguished Paper Award (2008) Committee & Society Roles: He serves on Japanese national committees including METI’s Power Safety Sub-committee and the Cabinet Office’s solar-energy evaluation panels, and is a board member of the Japan Solar Energy Society. Professional memberships include IEEE, IEEJ, JSCES and JSES. Laboratory & Projects: He heads the Wakao Laboratory within Waseda’s Department of Electrical Engineering, conducting projects on smart-grid PV management, magnetic-sensor optimisation for railway signalling, and net-zero-energy building renovation strategies.

• Artificial Intelligence
• Building Information Modelling & Processing
• Digital Twinning
• +3 more

Florian Noichl is a Group Lead in Spatial Computing at the Chair of Computing in Civil and Building Engineering at the Technical University of Munich . His work focuses on methods for creating digital twins of industrial environments, particularly car manufacturing plants, and includes collaborative projects with Siemens . He was a Visiting Researcher at the Department of Geomatics Engineering at the University of Calgary, Canada (May–August 2023). Research interests include Spatial computing Digital twinning Point cloud processing Artificial intelligence in construction Scan planning optimization GeoAI for retrofitting Recent publications emphasize automation in point cloud segmentation, material passport creation for steel structures, semantic facade analysis, and energy efficiency estimation using vision-language models. His work often integrates BIM with laser scanning and AI . Teaching includes Bau- und Umweltinformatik 1 (WS22/23, WS23/24) and Softwarelab . He supervises theses on topics like UAV mission planning Point cloud enrichment Image-based localization CAD model matching BIM design reviews Review activities cover journals such as Automation in Construction , IEEE Sensors Journal , and Remote Sensing . He is listed on OrcID : 0000-0001-6553-9806 Google Scholar ResearchGate Labs associated with include the BIM-Lab and Robotic Fabrication Lab . His work intersects digital construction with industrial AI applications.

• Carbon Dioxide Removal
• Underground Hydrogen Storage
• Volcanic Eruption Forecasting
• +3 more

David Dempsey is an Associate Professor in the Department of Civil and Environmental Engineering at the University of Canterbury's Faculty of Engineering, where he has been affiliated since December 2020. He leads the Subsurface Engineering Group, addressing critical challenges for a low-emissions future. His research integrates geomechanics, machine learning, and fluid dynamics across four key areas: Carbon Dioxide Removal : Designing systems to combust forestry waste and sequester CO₂ in geothermal reservoirs. Underground Hydrogen Storage : Modeling hydrogen injection/recovery in depleted gas fields. Volcanic Forecasting : Developing real-time ML systems for eruption prediction using seismic data. Induced Seismicity : Quantifying earthquake risks from energy projects. His recent publications (2021-2025) show a strong focus on machine learning applications in geohazards and energy storage, with themes including seismic forecasting, CO₂-hydrogen geostorage, and wildfire prediction. Numerical modeling and data-driven approaches dominate his methodology. He actively supervises 13+ graduate students on projects such as hydrogen geostorage, volcanic forecasting, and flood prediction. His group collaborates with industry on geothermal and seismic risk projects, leveraging real-time data from networks like GeoNet.